The evolution of digital networks in the last past years caused a fundamental shift in the customer traffic profile. Now, using the new networking technologies e.g. high speed packet switching networks allows the customer to integrate data, voice and video information digitally encoded, chopped into small packets and transmitted through the network. An efficient transport of mixed traffic streams on very high speed lines means for these new network architectures a set of requirements in terms of performance and resource consumption. One major requirement is the efficient management of the bandwidth allocation since transmission costs are likely to continue to represent the major expense of operating future telecommunication networks, as the demand for bandwidth increases driven by new applications and new technologies.
In digital transmission of voiceband signals, two types of signal can be present on a standard 64 kbps (thousand bits per second) PCM (Pulse Code Modulation) encoded digital voice channel, depending on whether it is voice (speech) or FAX and/or modem data (commonly referred to as voiceband data). When the signal is voice, bandwidth can be saved by using voice compression algorithms capable of reducing significantly the data rate in voice circuits without measurable loss of quality. Many voice compression algorithms rely on the fact that a voice signal has considerable redundancy, and then, the characteristics of the next few samples can be predicted from the last few ones. One of the most common voice compression algorithm based on the prediction method is the GSM (Group Special Mobile) technique. Using GSM compression technique allows a speech data stream to be compressed at a rate of 13 kbps compared to the initial bit rate of 64 kbps. Unfortunately applying such a compression algorithm (i.e. GSM) to voiceband data signals would increase dramatically the bit error rate. Consequently voiceband data should be either encoded at a higher bit rate so as to keep the data error rate in a permissible limit, or demodulated to extract the data, or kept transmitted at the initial 64 kbps.
Therefore, the necessity to apply selectively a high compression technique for bandwidth saving purpose to signals from a digital voice channel depending on whether they are speech or voiceband data, implies the use of an accurate speech/voiceband data discriminator.
Such speech/voiceband data discriminators already exist in the background art.
Publication "IEEE Transactions Communications, Vol. COM-30, No. 4, April 1982--Highly Sensitive Speech Detector and High-Speed Voiceband Data Discriminator in DSI-ADPCM Systems" by Yohtaro Yatsuzuka describes a high speed voiceband data discrimination technique. The discrimination between voiceband data and speech is based on a short-time energy, a zero-crossing rate and coefficients of an adaptive predictor. U.S. Pat. No. 5,295,223 issued on Mar. 15, 1994 to Saito (Japan) entitled "Voice/voice band data discrimination apparatus" discloses an apparatus for discriminating voice data so as to create statistical data and for discriminating voice/voice band data in digital speech interpolation and digital circuit multiplication equipment. A comparison is made between the dead zone width and the amplitude of the input signal so as to count only how many times the input signal crosses the width of each dead zone as the number of zero crosses.
U.S. Pat. No. 5,315,704 issued on May 24, 1994 to Shinta et al. (Japan) entitled "Speech/voiceband data discriminator" discloses an apparatus whereby input signals are processed to generate a plurality of signals having different features according to whether the input signals are speech signals or voiceband data signals, and these plural signals are entered into a neural network to be determined whether they have features closer to those of speech signals or of voiceband data signals. The classifying function of the neural network is achieved by inputting samples of speech signals and voiceband data signals and learning how to obtain correct classification results. Short time energies and zero crossing rates of input signals are both fed in parallel to the neural network for classification decision.
The prior art speech/voiceband data discriminators referred to above generally imply a complex processing and their discrimination accuracy is generally perfectible with maybe the exception of the last above mentionned prior art wherein a neural network is used for making the final decision but which is accordingly complex to implement.
The speech/voiceband data disclosed is the present application offers a high discrimination accuracy while requiring a low computing power, which makes it easy to implement and particularly suitable for applications wherein many voiceband channels have to be processed simultaneously.